LaVPR：语言与视觉用于位置识别的基准测试
LaVPR: Benchmarking Language and Vision for Place Recognition
AI总结：
LaVPR通过引入超过65万条自然语言描述扩展现有VPR数据集，研究多模态融合与跨模态检索，证明语言描述在视觉退化条件下提升性能，尤其对小模型效果显著。
摘要：
视觉位置识别（VPR）在极端环境变化和感知混叠下常失效。标准系统无法仅凭语言描述实现盲定位，这在应急响应等应用中至关重要。为解决这些问题，我们引入LaVPR，一个大规模基准，扩展现有VPR数据集超过650,000条丰富的自然语言描述。利用LaVPR，我们研究了两种范式：多模态融合以增强鲁棒性，以及跨模态检索用于基于语言的定位。结果表明，语言描述在视觉退化条件下带来一致提升，对小模型影响最大。值得注意的是，添加语言使紧凑模型能与更大视觉模型性能相当。对于跨模态检索，我们使用低秩适应（LoRA）和多相似性损失建立基线，其在视觉-语言模型中显著优于标准对比方法。最终，LaVPR使一类新的定位系统得以实现，既具有对现实世界随机性的鲁棒性，又适合资源受限部署。我们的数据集和代码可在https://github.com/oferidan1/LaVPR获取。
英文摘要：
Visual Place Recognition (VPR) often fails under extreme environmental changes and perceptual aliasing. Beyond these limitations, standard systems cannot perform 'blind' localization from verbal descriptions alone, a capability critical for applications such as emergency response. To address these challenges, we introduce LaVPR, a large-scale benchmark that extends existing VPR datasets with over 650,000 rich natural-language descriptions. Using LaVPR, we investigate two paradigms: Multi-Modal Fusion for enhanced robustness and Cross-Modal Retrieval for language-based localization. Our results show that language descriptions yield consistent gains in visually degraded conditions, with the most significant impact on smaller backbones. Notably, adding language allows compact models to rival the performance of much larger vision-only architectures. For cross-modal retrieval, we establish a baseline using Low-Rank Adaptation (LoRA) and Multi-Similarity loss, which substantially outperforms standard contrastive methods across vision-language models. Ultimately, LaVPR enables a new class of localization systems that are both resilient to real-world stochasticity and practical for resource-constrained deployment. Our dataset and code are available at https://github.com/oferidan1/LaVPR
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
